{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.optimize as opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./ex2data1.txt', delimiter=',', header=None, names=['1','2','3'])\n",
    "data = df.insert(0,'0',1)\n",
    "X = df[['0','1','2']]\n",
    "y = df['3']\n",
    "XX = X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>34.623660</td>\n",
       "      <td>78.024693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>30.286711</td>\n",
       "      <td>43.894998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>35.847409</td>\n",
       "      <td>72.902198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>60.182599</td>\n",
       "      <td>86.308552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>79.032736</td>\n",
       "      <td>75.344376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1</td>\n",
       "      <td>83.489163</td>\n",
       "      <td>48.380286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1</td>\n",
       "      <td>42.261701</td>\n",
       "      <td>87.103851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1</td>\n",
       "      <td>99.315009</td>\n",
       "      <td>68.775409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1</td>\n",
       "      <td>55.340018</td>\n",
       "      <td>64.931938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1</td>\n",
       "      <td>74.775893</td>\n",
       "      <td>89.529813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    0          1          2\n",
       "0   1  34.623660  78.024693\n",
       "1   1  30.286711  43.894998\n",
       "2   1  35.847409  72.902198\n",
       "3   1  60.182599  86.308552\n",
       "4   1  79.032736  75.344376\n",
       ".. ..        ...        ...\n",
       "95  1  83.489163  48.380286\n",
       "96  1  42.261701  87.103851\n",
       "97  1  99.315009  68.775409\n",
       "98  1  55.340018  64.931938\n",
       "99  1  74.775893  89.529813\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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   ],
   "source": [
    "pos = df[df['3'] == 1]\n",
    "neg = df[df['3'] == 0]\n",
    "plt.figure()\n",
    "plt.title('Sctater plot of training data')\n",
    "plt.xlabel('Exam 1 score')\n",
    "plt.ylabel('Exam 2 score')\n",
    "plt.plot(pos['1'],pos['2'],'ro',label='Admitted')\n",
    "plt.plot(neg['1'],neg['2'],'b^',label='Not Admitted')\n",
    "plt.legend(loc=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def featureScaling(data:pd.DataFrame)->pd.DataFrame:\n",
    "    data_f = data\n",
    "    # 第0列包含了1，所以要从第一列开始\n",
    "    for col in range(1,data.shape[1]):\n",
    "        data_col = data_f.iloc[:,col]\n",
    "        data_col = (data_col-data_col.min()) / (data_col.max() - data_col.min())\n",
    "        data_f.iloc[:,col] = data_col\n",
    "    return data_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(z):\n",
    "    return 1/(1 + np.exp(-z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#逻辑回归 代价函数\n",
    "def cost_function(theta,X,y) -> np.ndarray:\n",
    "    first = (-y) * np.log(sigmoid(X @ theta))\n",
    "    second = (1 - y)*np.log(1 - sigmoid(X @ theta))\n",
    "    return np.mean(first - second)"
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   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "   </g>\r\n",
       "  </g>\r\n",
       " </g>\r\n",
       " <defs>\r\n",
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       "   <rect height=\"217.44\" width=\"334.8\" x=\"30.103125\" y=\"7.2\"/>\r\n",
       "  </clipPath>\r\n",
       " </defs>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-10,10,20)\n",
    "plt.plot(x,sigmoid(x), 'r.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def get_theta(Xi,yi, a, iterator) -> np.ndarray:  # 梯度下降求theta\n",
    "#     X = np.matrix(Xi)\n",
    "#     y = np.matrix(yi).T\n",
    "#     theta = np.matrix(np.zeros((X.shape[1],1)))  # 初始化一个 θ 临时矩阵(1,n)\n",
    "#     cost = np.zeros(iterator)  # 初始化一个ndarray，包含每次epoch的cost\n",
    "#     m = X.shape[0]  # 样本数量m\n",
    "\n",
    "#     for i in range(iterator):\n",
    "#         # 利用向量化一步求解\n",
    "#         theta= theta - (a / m) * X.T * (sigmoid(theta, X) - y)\n",
    "\n",
    "       \n",
    "#         cost[i] = cost_funtion(theta,X,y)\n",
    "#     return theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient(theta, X, y):\n",
    "    return (X.T @ (sigmoid(X @ theta) - y))/len(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta = np.zeros(X.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -0.1       , -12.00921659, -11.26284221])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gradient(theta,X.values,y.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_f = featureScaling(X)\n",
    "\n",
    "Xi = X.values\n",
    "yi = y.values\n",
    "result = opt.fmin_tnc(func=cost_function, x0=theta, fprime=gradient, args=(Xi,yi))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-25.16131864,   0.20623159,   0.20147149]), 36, 0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     fun: 0.20349770158947458\n",
       "     jac: array([9.01809898e-09, 8.79718604e-08, 4.78760884e-07])\n",
       " message: 'Local minimum reached (|pg| ~= 0)'\n",
       "    nfev: 36\n",
       "     nit: 17\n",
       "  status: 0\n",
       " success: True\n",
       "       x: array([-25.16131864,   0.20623159,   0.20147149])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#与fmin_tnc方法一样， minimize函数可以 设置method方法调用其他方法。\n",
    "res = opt.minimize(fun=cost_function, x0=theta, args=(Xi, yi), method='TNC', jac=gradient)\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对求的theta进行测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(x, theta):\n",
    "    res = sigmoid(x @ theta)\n",
    "    return [1 if n>= 0.5 else 0 for n in res]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# x = np.array([1,30,40])\n",
    "# theta_res = result\n",
    "\n",
    "correct=[1 if a==b else 0 for (a,b) in zip(test(X.values,result[0]), y.values)]\n",
    "accuracy = np.sum(correct) / len(correct)\n",
    "accuracy\n",
    "#当前模型的正确率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决策边界"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_theta = result[0]\n",
    "x1 = np.linspace(20,120,num=100)\n",
    "x2 = np.array([-(res_theta[0] + res_theta[1] * x1[i])/res_theta[2] for i in range(len(x1))])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "plt.figure()\n",
    "plt.title('Sctater plot of training data')\n",
    "plt.xlabel('Exam 1 score')\n",
    "plt.ylabel('Exam 2 score')\n",
    "plt.plot(pos['1'],pos['2'],'ro',label='Admitted')\n",
    "plt.plot(neg['1'],neg['2'],'b^',label='Not Admitted')\n",
    "plt.plot(x1,x2,'g') #决策分割\n",
    "plt.legend(loc=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 正则逻辑回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>X0</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>114</th>\n",
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       "      <td>0.494880</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>1</td>\n",
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       "      <td>0.999270</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>116</th>\n",
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       "    <tr>\n",
       "      <th>117</th>\n",
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       "      <td>0.632650</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>118 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     one        X0        X1  X2\n",
       "0      1  0.051267  0.699560   1\n",
       "1      1 -0.092742  0.684940   1\n",
       "2      1 -0.213710  0.692250   1\n",
       "3      1 -0.375000  0.502190   1\n",
       "4      1 -0.513250  0.465640   1\n",
       "..   ...       ...       ...  ..\n",
       "113    1 -0.720620  0.538740   0\n",
       "114    1 -0.593890  0.494880   0\n",
       "115    1 -0.484450  0.999270   0\n",
       "116    1 -0.006336  0.999270   0\n",
       "117    1  0.632650 -0.030612   0\n",
       "\n",
       "[118 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 =pd.read_csv(\"./ex2data2.txt\",header=None,prefix='X')\n",
    "df2.insert(0,'one',1)\n",
    "df2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26d72308b48>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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     "metadata": {
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   ],
   "source": [
    "pos = df2[df2['X2'] == 1]\n",
    "neg = df2[df2['X2'] == 0]\n",
    "\n",
    "plt.plot(pos['X0'],pos['X1'],'bo',label='Accepted')\n",
    "plt.plot(neg['X0'],neg['X1'],'r^',label='Rejected')\n",
    "plt.xlabel('Text 1 core')\n",
    "plt.ylabel('Text 2 core')\n",
    "plt.legend(loc=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fearture_mapping(x1,x2,power):\n",
    "    data = {\"f{}{}\".format(i-p,p) : np.power(x1,i-p)*np.power(x2,p) for i in np.arange(power+1) for p in np.arange(i+1)}\n",
    "    return pd.DataFrame(data)"
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  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.375000</td>\n",
       "      <td>0.50219</td>\n",
       "      <td>0.140625</td>\n",
       "      <td>-0.188321</td>\n",
       "      <td>0.252195</td>\n",
       "      <td>-0.052734</td>\n",
       "      <td>0.070620</td>\n",
       "      <td>-0.094573</td>\n",
       "      <td>0.126650</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017810</td>\n",
       "      <td>-0.023851</td>\n",
       "      <td>0.031940</td>\n",
       "      <td>2.780914e-03</td>\n",
       "      <td>-3.724126e-03</td>\n",
       "      <td>0.004987</td>\n",
       "      <td>-0.006679</td>\n",
       "      <td>0.008944</td>\n",
       "      <td>-0.011978</td>\n",
       "      <td>0.016040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.513250</td>\n",
       "      <td>0.46564</td>\n",
       "      <td>0.263426</td>\n",
       "      <td>-0.238990</td>\n",
       "      <td>0.216821</td>\n",
       "      <td>-0.135203</td>\n",
       "      <td>0.122661</td>\n",
       "      <td>-0.111283</td>\n",
       "      <td>0.100960</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026596</td>\n",
       "      <td>-0.024128</td>\n",
       "      <td>0.021890</td>\n",
       "      <td>1.827990e-02</td>\n",
       "      <td>-1.658422e-02</td>\n",
       "      <td>0.015046</td>\n",
       "      <td>-0.013650</td>\n",
       "      <td>0.012384</td>\n",
       "      <td>-0.011235</td>\n",
       "      <td>0.010193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   f00       f10      f01       f20       f11       f02       f30       f21  \\\n",
       "0  1.0  0.051267  0.69956  0.002628  0.035864  0.489384  0.000135  0.001839   \n",
       "1  1.0 -0.092742  0.68494  0.008601 -0.063523  0.469143 -0.000798  0.005891   \n",
       "2  1.0 -0.213710  0.69225  0.045672 -0.147941  0.479210 -0.009761  0.031616   \n",
       "3  1.0 -0.375000  0.50219  0.140625 -0.188321  0.252195 -0.052734  0.070620   \n",
       "4  1.0 -0.513250  0.46564  0.263426 -0.238990  0.216821 -0.135203  0.122661   \n",
       "\n",
       "        f12       f03  ...       f23       f14       f05           f60  \\\n",
       "0  0.025089  0.342354  ...  0.000900  0.012278  0.167542  1.815630e-08   \n",
       "1 -0.043509  0.321335  ...  0.002764 -0.020412  0.150752  6.362953e-07   \n",
       "2 -0.102412  0.331733  ...  0.015151 -0.049077  0.158970  9.526844e-05   \n",
       "3 -0.094573  0.126650  ...  0.017810 -0.023851  0.031940  2.780914e-03   \n",
       "4 -0.111283  0.100960  ...  0.026596 -0.024128  0.021890  1.827990e-02   \n",
       "\n",
       "            f51       f42       f33       f24       f15       f06  \n",
       "0  2.477505e-07  0.000003  0.000046  0.000629  0.008589  0.117206  \n",
       "1 -4.699318e-06  0.000035 -0.000256  0.001893 -0.013981  0.103256  \n",
       "2 -3.085938e-04  0.001000 -0.003238  0.010488 -0.033973  0.110047  \n",
       "3 -3.724126e-03  0.004987 -0.006679  0.008944 -0.011978  0.016040  \n",
       "4 -1.658422e-02  0.015046 -0.013650  0.012384 -0.011235  0.010193  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3 = fearture_mapping(df2['X0'].values,df2['X1'].values,6)\n",
    "data3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradientReg(theta, X, y, a=1):\n",
    "    #不惩罚第一个\n",
    "    m=X.shape[0]\n",
    "    reg = (a/m) * theta\n",
    "    reg[0] = 0\n",
    "    return gradient(theta,X,y)+reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def costFunReg(theta,X,y,a=1):\n",
    "    _theta = theta[1:] #第一项不惩罚\n",
    "    reg = (a/(2*len(X))) * (_theta @ _theta) #正则参数\n",
    "    \n",
    "    return cost_function(theta,X,y) + reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.90267454,  0.33721089,  0.76006404, -1.39757946, -0.51417075,\n",
       "        -0.91389985,  0.01516214, -0.21926018, -0.22677642, -0.16219637,\n",
       "        -1.01270257, -0.04169398, -0.39984069, -0.14458017, -0.82296284,\n",
       "        -0.20346047, -0.13186937, -0.04837714, -0.17183934, -0.17077937,\n",
       "        -0.38820995, -0.72773035,  0.00607686, -0.19391898,  0.00314606,\n",
       "        -0.21203169, -0.06947222, -0.69320885]),\n",
       " 25,\n",
       " 1)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta3 = np.zeros(data3.shape[1])\n",
    "X3 = data3.values\n",
    "y3 = df2['X2'].values\n",
    "reslut2 = opt.fmin_tnc(func=costFunReg, x0=theta3,fprime=gradientReg,args=(X3, y3, 2))\n",
    "#gradientReg(theta,X3,y3,2)\n",
    "# theta3.shape,X3.shape, y3.shape\n",
    "reslut2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8305084745762712"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_theta = reslut2[0]\n",
    "predictions = test(X3,final_theta)\n",
    "correct = [1 if a==b else 0 for (a, b) in zip(predictions, y3)]\n",
    "accuracy = sum(correct) / len(correct)\n",
    "accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(-1, 1.5, 250)\n",
    "xx, yy = np.meshgrid(x, x) #生成方阵\n",
    "# xx.reval() 将xx方阵降为 一维 yy.ravel()同理\n",
    "# 再将网格上对应的x , y 坐标，进行特征映射\n",
    "z = fearture_mapping(xx.ravel(), yy.ravel(), 6).values\n",
    "# 最后将特征映射后的数据集与theta进行向量乘积，得到最后的 等高线的等高值\n",
    "z = z @ final_theta\n",
    "#z = z.reshape(xx.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -5.62156343,  -5.50740428,  -5.39655278, ..., -46.52030526,\n",
       "       -47.2664742 , -48.03020215])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.9, 1.2)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j3j1s2LOHTfv2HmHi69e9ByNy8xidn8/ovD6Mys9ncK/ezo54XAwTNhIbUzABYAomOBqam1m8bSsfbFzP/M2bWLvbM5u4R1o64/r1Z1y/fhT37ceYPn3pnZl51Pmhjjx8CXUUEu65ndKJI72ppYUt+/dRVlXF2t1VrNm9i9W7drFuz26aWlrI37efB554mof/85cMHjmSE47px7hj+tM3nHodNtfJiBChKhhz8htHUN/UxIebNvDPtav5cOMGqhsaSEtOZvyAgUwZfRwTCgYxKi+/0yistiOPQyWSITglE06hsrvu8j/6caREQCeO9JSkJIp69aaoV2/OHzqsdX99UxNlu6tI++lPGbpuPZc/9zw/v/xSGlo8Lz79e/TgpH4DOLn/AMYXDGRo75zAndYWJmzEGDaCMVBVvtxezvMrlvNm2VoONNSTk5HJeUOGcv6QYZw2cBCZqcGZupwaPYR7HSdGUUcRriO9zfn1a9bwTUoyX1aU82VFOaUV21r9O3lZWYwvGMjEgYWcMWgw/Xr0CFP4AGRzI8TZiGni0kQmIhcBM4Fk4GFV/VOb4/cCk7ybWUAfVe3lPdYMLPMe26yql3fWnymYI6lpbOTFb1bw9NdLWLO7iqzUVC4eNoLLR4xkwsBBpIQxV8Qp/0c4PpiIEa4jvZPzVZVN+/by2dYtLNy2hYVbtlBZ41E4I3JyOXtwEecUDeWkfv2dn89z883w97/DjTeaWc1oJe4UjIgkA2uA84GtwOfAlarqtw6oiPwEOFFVf+jdrlbVoAzWpmA8VNXU8NiSL3l62VL21dcxpk9frhozlktGjHRsroeT/o+IjEJCJVxHegjnqyqrq3bxyeaNfLhxI6XlW2lsaSEnI5NzhgzhoqEjmDiokLTkZOe+mwUHGD7Eow/mFKBMVdcDiMgzwGSgvULTVwK/b+eYEQD76up46IvFPLH0K+qamrhg6HB+PK6Ecf36O96Xk/6Pq66KoeJkHeXbCuSNP4TzRYSReZ4IvR+PO5kD9fV8vGkj725Yx1tla3lh5Qqy09O5cOhwLj92JBMKBoUWneavnEOgoxgzrRl+cHMEMwW4SFV/5N2+BjhVVW/x07YQWAQUqGqzd18TsARoAv6kqq+008/1wPUAgwYNOinWMpdGg8bmZp74egn3L17E/vo6Ljt2JD85eTxDc3Ij2m9MjTyc4sQTYcmSo/cXFweWKiXc89tQ39TE/C2beGPtGt5ZV0Z1YwP9uvfg2yNHM3X0GAp79QrsQuGOzMy0ltDEo4lsKnBhGwVziqr+xE/b/8SjXH7is6+/qpaLyBDgfeBcVV3XUZ9dykTmfaP86r57uXXF16zdXcWZgwbznxPPZFRefsjXszfU2KWuqZF316/jpVUr+XjTRlpUOW3gIK46/gTOHzKsY59aOCHOZlpLeOIxXf9WYKDPdgFQ3k7bacBc3x2qWu5drwc+BE50XsT4pen222n55BOW/+Qn1DQ2MvvSyTx+xXdCUy7gWhncp5/2+HOSkjzraBXlc6vfcMhISeXSESN59PJ/Yf61P+b/TjidjXv38G9v/IOzHn+Yh0oXs7++nVQq4YQ4R6C+vZEghFKlzIkFj/9nPVAEpAFLgeP8tDsW2Ih3tOXd1xtI937OA9YCozvrs6tUtFyzbJnWpaaqgjakp+nBzZvDu6BvNcxgq2AGyFNPqRYWqop41k895Vmyso4sOpiV5dkfSdzqtzOZ2t6fQGhqbtZ5ZWv1ey8+p0UzZ+iYv83Uuz7+QLcfOOCMYE5USjViHkKsaOmagvHIzLfwRJKtA27z7rsDuNynzX/h8bH4nncanhDlpd71dYH0F9MKprxc9cwzw/7HfHXVSv3fM07X+uRkdaxsbYTL4Lb3QM/NPXLfoaWw0P81QnkA+6OwMPB+o4FTCm/lzh36s7de12Gz7tFj779Xb//wPd1RHaai8f1tHFqiWCrZiA5xqWCivcS0grnpJtWkpJD/MVtaWvS/F87XU+74ndZ7Ry+OvFFG4Q21vQd6e4vIkec7PeIQCazfaOGEwvNVwAMGNumlty7RYbPu0VEP3Kd3f/qJ7qurC024CNW3N2KLUBVMAlddiiMqKuCxxzx27Mceg+3bgzq9uaWF295/h78uXsS9n39BatsQ1XDs4lEogxtI2hdfBg06cvu2244MhwbP9m23hSZP2+t3tj/ShJMuBw5PVt20yfP037YlmfdnncAtGTdw/pBh/K30M8594hH+d9lSmoPNAvrVV/7fAyJdfKyiAs46K+j/FSO6mIKJBcJwkja3tPAf777NMyuWcXPJqUzYsg1xMh9VFPJbtffgzs2FthlqUlOPnksT7gO4LXfd5Zmz44tjOcxCIFyF154Cvveubsy86BJenXY1Q3vn8psP3uXbz/0vS3fEwUPbpaATI0hCGfbE6xKTJrIwTFAtLS366/fmadHMGXr/4oVREDYytGfias+839b0FQmfiZM+nXAJ1wQYiMmvpaVFX1v9jZ768IM6ZOYMveOj9/VgQ4NzX8IhH2PrtSIcdGIcCeaDiVMFE4aT9K+fLdSimTP0z/M/DqnrWHuItpUlUMURi1FfThPO3yoYBbyvrk5/+/47WjRzhp79+MP6Rfk2Z75AmD7Go64VwaAT42hMwcSrggnRSfpW2RotmjlD//2tf2pLS0vQ3cbDQzkYZ3ssKctYI5S/9cItm3Xio7N16Kx7dOaiBdrU3By6AE6OOCws2hVMwcSrggmBTXv36PEPztIrnnlK6xobQ7pGrIXi+iMeZIw4DpmWQlHA++vq9P+89U8tmjlDr3rpOd118GBonTs54rCwaFcIVcGYkz/OaG5p4d/ffoMkEe6/+DLSU0LLV+q0YzwSxJqz3RUccmZfdZUni3VLi2cdSE64Hunp/PcFF/Pn8y7ki/JyLn/mKVbs3BFcx4ciJA8FijQ0hBQp2YoVVYsrTMHEGY8u+YKvtldw7oFvc/rY7JBTmcRaKK4/rrrKU/elsNBTR6aw0OU6MNEmzPB1JxARpo4ew/NTpwEw9YVneGdd2dFythcy7HSYu1th0UZImIKJI7Yd2M+9ixYweNOZPPT7/q3zGg6VIw5GycTL6CCUN++Ewefh3NzYzBPD73QtN9qYPn15ZdpVHJubx43/fJWnly09Us72Rlk24ujahGJXi9cl3n0wP3vrdR15/306YGCzI74Jc4w7Q0Tuox9n9kEytS8VrgZk1DQ06HWvvqRFM2foA4sXWchwFwHzwSQ2Kyt38trqVVx34kmUb/X/ZwvWf9KlRwcO0XaWfCijSb/4MS0l0cxv8YwSwslUEA6Zqak8eMnlXH7sSGYsnM+yW/7NMikb7WIKJk6YtXghPdLSuf6kkrjwn0Qbt9LrO52mphU/pqUMGjiNw6YltwIyUpOTuef8i5mefwzD//EP5xz4RsJhCiYOWL9nN++sK2P6CSeSnZ4RN/6TSOBPkURsFBEAEYvG83FmDy5UBM8yjsPObDdfKJKTkvjtx/NJ1jYHbBRj+GAKxk0CTNj31NdLSElK4pqxxUDXja5qT5H87GcRGkUEQHsP+aQk50ZTsfpCkbRoEalNTUfuNAe+4Usojpt4XWLOyR9A+oy6xkY94aG/6k/ffD2KgsUm4ab1jwT+Zsm3Xdo65EMJCojlgIz9dXV60VOP69gH/6rrdle5LY4RATAnf5wR4ByH9zasZ399PVNGHxdlAWOPcNP6R4K2o8nk5KPb+I6mQjXnxXJARo/0dP7nsitITUripn++xsG2YclGl8UUjFsEmKL/jbWryc3M4rSCLuzB99JRWn83TUi+D//2yqkcUo4RCwpwmYLsnsy8+BLW7dnN7z58z21xjBjBVQUjIheJyGoRKRORW/0c/4GIVIrIEu/yI59j00VkrXeZHl3JwyTA9Bn1TU18tGkDFwwdRnJSO38qFwovuRWx1Z4vYubM2PFJdRbhFw8pekLl9IGF/OSU8by8aiWvrPomvItZQbHEIBS7mhMLkAysA4YAacBSYHSbNj8A7vdzbg6w3rvu7f3cu7M+Y8YHE2DCvk83b9KimTP03XVlHV/LqTToAeB2FuZY9kWodn5/4i2BZ7D3u7G5Wac89796wkN/1YoD+0PvOMq/a6NjiLdsysAE4G2f7V8Bv2rTpj0FcyXwd5/tvwNXdtZnzCiYAFP0z1jwiQ6bdY8eqK/3fx0XZlHH2wPSDTp6KLutoIMhVFk37Nmtox64T3/02kshlZKw7ACxR6gKxk0T2QBgi8/2Vu++tnxHRL4WkRdEZGCQ5yIi14tIqYiUVlZWOiF3+ASYsK+0fBuj8/vQPS3N/3WCKLXslFkrFBOPWyY1t+jIIR9PIeah+osG9+rNz8efznsb1jNvfVnHjf0RRglxI7ZwU8GIn31tp239AxisqmOBd4E5QZzr2ak6W1VLVLUkPz8/ZGGjTXNLC8t27mBcv/7+GwSRBt3JiYjBZhFwcxJkrBLLEWG+hOMv+kHxOEbm5XPnxx9Q29gYeKdOp/c3XMVNBbMVGOizXQCU+zZQ1SpVrfdu/g9wUqDnxjsb9+6hprGRMX36+m8QRBp0JyOXgp30l6hRU12BcFISpSQl8fszJ1F+4ACPfPVF4J06nd7fcBU3FcznwHARKRKRNGAa8JpvAxHp57N5OXAoNOVt4AIR6S0ivYELvPsShtVVuwA4NjfPf4Mg0qA7GbkUrIknkaOmEp1wMwicWjCQC4YOY/YXn7O7tsZvm7bm091vWHr/RMI1BaOqTcAteBTDN8BzqrpCRO4Qkcu9zX4qIitEZCnwUzxOf1R1N3AnHiX1OXCHd1/CsH7PHgCG9s7x3yCIwktOJ8cMxsRjiTnjFyf8Rb+YMJGapkYeKl181DF/5tOBlV/x9FNWUCxREE+AQNegpKRES0tL3RYjIP7j3bf4ZNMmFl53Q9jXOvSP7GuqysqKjnPZzb6N2OD/znuTN8vW8MkPfkyuz5Bo8GCPUmlLYaHnxcWIHUTkC1UtCfY8m8kfo5QfOEC/7j0cuZabkUvxFDVlRIabSk6hvqmJJ74+chRi5tPExxRMjFJ58CB9undz7HpuRi7FYtRUVwuddpNhObmcN2QoT3695IiIMjOfJj6mYGKU3bW15GZmdd7QCBoLnY4+Pyw+ib11dby2ZlXrvlgtQ2A4hymYGERV2V9fR3Z6utuiJCQWOh19ThlQwIjcPJ5etrR1n5lPE59OFYyIZIrIr0TkIe/2MBG5OPKidV0amptpbGlpfwa/ERZm+48+IsKVY45n+c4drKzc2bo/Fs2nhnMEMoJ5FM/M+Yne7XLgDxGTyKC+2VMlMCMl1WVJEhOz/bvD5GNHkZaUzAvfrHBPCMvSHFUCUTDDVfUPQCOAqtbgP1WL4RCNzZ6ZzKntpehPUKLleDfbvzv0ysjk7MFF/HPNaprbK5wTae68E+bPt8wAUSKQJ1iDiGTgzfUlIkWAlayLIIdmJol0HT0eTce72f7d47IRI6msOcjn5dui33mAVWQN5whEwdwBvAUUiMgc4AM8qfWNCHFIrbSdBJvIobXRdryb7d8dJhUNIT05hbfXrY1+55alOep0qGDE8wq9FJgK/Bh4GThFVa0magRJ9RZ2b2hubt2X6KG15njvGmSlpjJx0CDeXb/uqBeoiGJZml2hQwXjLTTzuqpWquqrqvqKqu7s6JwuicOOw3Svgqn3UTCxFlrr9GjKHO9dh0mDh7DtwH7KdkcxfaBlaXaFQExki0VkXMQliWccdhymJSeTLEJN42FXVyy94UdiNGWO967DWYVFAHyyeWP0Og0i+7jhHIEomIl4lMxqEflSRL4SkS8jLVjcEAHHoYjQIz2dA/X1rfti6Q0/EqMpc7x3HQZkZ1PYsxcLtkTx7SiI7OOGcwSiYK4AjgW+hccXM8W7NiA4x2EQprSe6Rnsra9r3Y6lN/xIjabM8d51mFAwkMXlW90LVzaiQqcKRlXXAZnA+d4lw7vPCNZxGIQpLTczk921ta3bsfSGH0ujKSM+OWVAAdUNDazxFtYLGpswGRcEkirmFuA5YJB3eU5Ebo60YHFBMI7DIE1peVnd2NXGDhUrb/ixNJoyootTwR0nHtMfgK+2V4R2AZswGRcEYiK7Hk9o8q9V9dfAqVBzyFAAACAASURBVMCNkRUrTgjGcRhkDH6fbt3YUV3tkKDOEkujKSN6OBncMahnT3pnZPD1jhBGIDZhMm4IRMEI3jQxXhpxKFWMiFzkDR4oE5Fb/Rz/uYisFJGvReQ9ESn0OdYsIku8y2tOyBM0gToOQ4jB79ejB/vq6zjYVoHFCLEymjKih5PBHSLCcfl9WVEZwqwHmzAZNwSiYJ4EFonIb0TkN8ACYE64HYtIMvAAcDEwGrhSREa3afYVUKKqY4EXgL/4HKtV1WLvcnm48kSUEGLwB2b3BGDL/n2RlMwwAsbp4I7R+fmsraqi0We+V6fYhMm4IhAn/1/wmMlqgFrgRlWd4UDfpwBlqrpeVRuAZ4DJbfr+wJtcE2ARUOBAv9EnhBj8gT17AbBp395ISmYYAeN0cMfIvHwaWprZuDeI37hNmIwrAnHynwx8o6r/rar3AKtEpMSBvgcAW3y2t3r3tcd1wJs+2xkiUioii0TkivZOEpHrve1KKysrw5M4VEKIwS/q1RuADXv2REtKILHznRnh4XRwx7CcXADW7q4K/CSbMBlXBGIim41n9HKIg8DfHejbnx/Hb3IiEbkaKAHu9tk9SFVLgO8B94nIUH/nqupsVS1R1ZL8/PxwZY4a2enp5Gd1Y92e8NJpBKMwEj3fmREeTgd3DOmdA8D6YH7jNmEyrghEwSSpauuY1PvZiUpYW4GBPtsFeIqZHYGInAfcBlyuqq1T21W13LteD3wInOiATDHF8Nzc4N7u2hCswoi1fGdG7OFkcEdWairHdOtuZuAEJhAFs0FEbhKRZBFJEpF/AzY60PfnwHARKRKRNGAacEQ0mIiciGe0dLlvkk0R6S0i6d7PecDpwEoHZIopRuTmsbZqV8iznYNVGLGU78zoGhT07MmWfRbIkqgEomBuAM4FdniXs/Ck7g8LVW0CbgHeBr4BnlPVFSJyh4gcigq7G+gOPN8mHHkUUCoiS/HUp/mTqiacghmZm0dtU1PIb3jBKgyboW9EmwE9simv3u+2GEaECCSKbIeqTlHVPFXNV9XvquoOJzpX1TdUdYSqDlXVu7z7fqeqr3k/n6eqfduGI6vqAlU9XlVP8K4fcUKeWGNMn74ALN8Z2u0OVmG4PUPfAgy6Hsd0786O6mpaolkbxogaXavoe5wxPCeX9OQUloYy25ngFYabM/QtwCD+CeUF4Zju3WlsaTki756ROJiCiWFSk5MZ06cPS0PM1xSKwnBrhr4FGMQ3ob4g5Gd1A2BXzcEoSGlEG1MwMc64fv1ZvnMn9U1NIZ0fLyldLMAgvgn1BSEn0zPErqqt6bihEZd0qGBEZJiInCUiWW32nx9ZsYxDlPQbQENLM1/vTOxUGBZgEN+E+oLQKyMDgH11dR03NOKSdhWMNxz5LeCXwAoRucTn8J8jLZjh4aT+nrTmi7dtc1mSyOJ2gIERHqG+IPRM9yoYn+qtRuLQ0QjmRuBEVb0UT5jynd7aMOBQNmWjc3Iyszg2N49FWxPbVmQlAOKbUF8QeqSnA1DdYAomEelIwSSp6gFonS1/NjBZRP7SwTlGBDht4CBKy8tD9sPEC/HiLzKOJtQXhKxUT1KQgw2NHTeMRayqZqd0pGB2isjYQxuquh/4Fp6ULmPbPctwnImDCqlvbmJx+Va3RTGMdgnlBSFJhPTkFOqa4lDBWFXNTulIwUwHjkg/rKqNqvo94JyISmUcwakDBpKWlMzHmza6LYphOE56SjL1wdSEcZJQRyFWVTMg2lUwqrpZVf1OwFDVjyInktGWrNRUThlQwIcbN7gtimE4TmpSEg1uKZhQRyFWVTMgbB5MnHBO0RDW7dnNhr3RrQ9jGJEmOSnJnVQxoY5CrKpmwJiCiRPOG+Ipd/Pu+jKXJTEMZxEEdUPBhDoKsaqaARNIRcs/BLLPCJNObMEF2T05Lr8Pb5etjbJghhFZFEUkyjMfwhmFWFXNgAlkBHORn32X+NlnhEMAtuALhw7ny+0VbK8+EEXBLMuxEVmaW1pIToqyMSWcUYhV1QyYjmby3yAiXwEjReRLn2UtnvothlMEaAv+1vARAPxz7ZqoiRZOlmNTTEYgNLa0kBLtEYyNQqJCR68NzwFTgX9614eW01V1WhRk6zoEaAse0juH4/L78I81q6ImWqhJDC39fmwSi0q/obmZ9JSU6HVYUQHZ2Z61jUIiSkdhyntUtQx4QFXX+Sw7RcSROdYicpGIrBaRMhG51c/xdBF51nv8MxEZ7HPsV979q0XkQifkcYUgbcGTjx3F1zu2s37P7qg8LEJNYmjp92OPWFT6LarUNTWREU0FYxMko0Yghs+7ROSvIpIpIvki8jKekUxYiEgy8ABwMTAauFJERrdpdh2wR1WHAffiTbLpbTcNOA6Pj+hv3uvFH0Hagi8bMZIkEX4zc2dUHhahJjG09PuxRywq/Tpv+qNDKWMijk2QjCqBKJgzgG3AV8AC4CVVvcKBvk8BylR1vao2AM8Ak9u0mQzM8X5+AThXPOEmk4FnVLVeVTcAZd7rxR9B2oL7du/OxIGFvHT/gKg8LEJNYhiMYopFs00iEotK/1CSy+5p6dHp0CZIRpVAFEw2cAKwFWgA+oozMYUDgC0+21u9+/y2UdUmYB+QG+C58UEIESnfPe546qu6+z3m9MMi1CSGgSqmWDTbJCqxWHNnvzdNf4+0tMh3ZhMko04gCmYx8IGqnodnlDAE+MSBvv0pqbazrdprE8i5nguIXC8ipSJSWllZ6a9J3HHekKGk5fgPVY7EwyKUJIaBKqZYNNskKlGpuRNkbq+93kJjvTIyHRSiHWyCZNQJRMFcqKqzAVT1oKreDPzOgb63AgN9tguA8vbaiEgK0BPYHeC5eGWeraolqlqSn5/vgNjuk5aczBW3bENSj8xAG2sFugJRTLFotklUolJzJ0gH+p7aWgB6Z0ZBwVhoctTpVMGo6gYRmSYitwGIyEA8pqpw+RwYLiJFIpKGx2n/Wps2r+HJ6gwwBXhfPTklXgOmeaPMioDheEZaXYYZP+9P3rR59O5bF9cFumLRbJPIRLTmTggO9F21nuFrbjQUjE2QjDqBpIq5H5gEXO3ddRB4KNyOvT6VW4C38UzcfE5VV4jIHSJyubfZI0CuiJQBPwdu9Z67As88nZV4yjr/m6q6lI7VHQb27MnkqQ0MvfNRahuaYrtAVwdmEyuVnECE4EDfVXMQgNzMrE5aGvFIICay01T1BqAOQFV3A4545FT1DVUdoapDVfUu777fqepr3s91qjpVVYep6ineypqHzr3Le96xqvqmE/LEFRUVzLjrT8iOHby6OsYTK3RgNrFSyQlCiA707dXV5GRkRneipRE1AlEwjSKShNeJLiK5QEvHpxidEm651TvvJPvzUn730Xwe/rLUnXTngRCA2cRKJScAITrQK6oPcEx3/xGRQWMljGOOjnKRHXqleAB4EcgXkduB+XgnPBphEM5sYu9DW1pauPiTT9m7aSPvb1jnvIxOYPMOugYhOtDL9+9nQHa2MzLYDP2Yo6MRzGIAVX0C+A0wA9gDTFXVZ6IgW+IS7mxin4d2kiq/+uAjHixd7E5NjY6weQddhxAc6KrK1gP7GZDdM/z+bYZ+TNKRgmmda6KqK1R1pqrep6rLoyBXYhPOW32bh7Y0NHDZp4vYsmY187dsioCwYWDzDowO2FVTQ01jI4U9HVAwNlKOSTpSMPki8vP2lqhJmGiE+1bv56GdrMp/vv8Rsz5bGFujGJt3YHTA+j27ASjqlRPehWykHLN0pGCSge5Aj3YWIxTCfav389CWhgYmlW/ni4pyPt60MXiZIuUctXkHRgeUeRXM0JwwFYyNlGOWjhRMhareoaq3+1uiJmGiEe5bfTsP7e4rVlC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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#\n",
    "pos = df2[df2['X2'] == 1]\n",
    "neg = df2[df2['X2'] == 0]\n",
    "\n",
    "plt.plot(pos['X0'],pos['X1'],'bo',label='Accepted')\n",
    "plt.plot(neg['X0'],neg['X1'],'r^',label='Rejected')\n",
    "plt.xlabel('Text 1 core')\n",
    "plt.ylabel('Text 2 core')\n",
    "plt.legend(loc=1)\n",
    "#\n",
    "plt.contour(xx, yy, z, 0)\n",
    "plt.ylim(-.9, 1.2) #限制y坐标范围为 -0.9~1.2\n",
    "\n"
   ]
  }
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